Background Stomata are tiny pores on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious and not high throughput. This impedes research on stomatal biology and hinders efforts to develop resilient crops with optimised stomatal patterning. We have developed a rapid non-destructive method to phenotype stomatal traits in three crop species: wheat, rice and tomato. Results The method consists of two steps. The first is the non-destructive capture of images of the leaf surface from plants in their growing environment using a handheld microscope; a process that only takes a few seconds compared to minutes for other methods. The second is to analyse stomatal features using a machine learning model that automatically detects, counts and measures stomatal number, size and aperture. The accuracy of the machine learning model in detecting stomata ranged from 88 to 99%, depending on the species, with a high correlation between measures of number, size and aperture using the machine learning models and by measuring them manually. The rapid method was applied to quickly identify contrasting stomatal phenotypes. Conclusions We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition and analysis. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.

Background: Stomata are tiny pores located on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and any variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious, which impedes research on stomatal physiology and hinders efforts to develop resilient crops with optimised stomatal patterning. We developed a rapid non-destructive method to phenotype stomatal traits in four species: wheat, rice, tomato, and Arabidopsis. Results: The method consists of two steps. The first step is to capture images of a leaf surface directly and non-destructively using a handheld microscope, which only takes a few seconds compared to minutes using other methods. This rapid method also provides higher quality images for automated data analysis. The second step is to analyse stomatal features using a machine-learning model that automatically detects, counts stomata and measures size. The accuracy of the machine-learning model in detecting stomata ranged from 89% to 96%, depending on the species. Conclusions: We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.